<![CDATA[Decision Support System (DSS) is a tool that helps organizations make objective and structured data-driven decisions. One of the commonly used methods in DSS is the Multi-Attribute Utility Theory (MAUT), which enables the evaluation and comparison of alternatives based on multiple criteria with predetermined weights. This study aims to implement the MAUT method to assess employee eligibility in the production sector by considering factors such as technical knowledge, operational skills, time management, workplace safety, and work quality. By applying MAUT, companies can rank employees based on utility scores calculated through normalization and weighting. Data were collected from 30 production employees using observations and interviews to assess performance based on 12 key indicators. The results indicate that the MAUT method provides structured and accurate outcomes in determining the most eligible employees according to the specified criteria. Furthermore, this method supports more transparent and efficient decision-making, particularly in employee evaluation, promotion, and human resource management. This study concludes that implementing MAUT significantly contributes to assisting manufacturing and industrial companies in objectively managing employees and provides a foundation for developing AI-based employee evaluation models and machine learning applications in the future]]>
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